Community science for mosquito surveillance in Canada: a toolkit for program design and implementation
Bibliographic record
Abstract
SETTING: Climate change in Canada has expanded suitable habitats for both native and invasive mosquitoes, heightening the need for more robust and adaptable surveillance systems. Traditional surveillance methods face significant logistical and geographical challenges, particularly in remote or underserved areas, emphasizing the importance of innovative strategies that complement existing approaches. INTERVENTION: We developed a community-based mosquito surveillance toolkit, informed by a systematic review and validated by a multidisciplinary expert group. The toolkit includes a structured guide for developing a community science project, including how to design an effective mosquito surveillance program through data collection, volunteer engagement, and evaluation. It also features a quick reference guide and a 90-s animated video to facilitate understanding and engagement. OUTCOME: The toolkit was introduced at a national community meeting, with 69 participants bringing together public health professionals, educators, scientists, and community leaders from across Canada in February 2025. Participants expressed enthusiasm for using the toolkit to enhance local monitoring efforts, and discussions at the meeting helped identify key opportunities and challenges for adoption. IMPLICATIONS: Organizations and public health authorities now have a clear, evidence-based guide to designing, implementing, and evaluating community-driven initiatives. This toolkit not only helps ensure that volunteer participation is meaningful and effective, but it also enhances the capacity to respond to emerging challenges in vector control, laying the groundwork for sustainable, community-driven surveillance efforts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.103 | 0.093 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.008 | 0.014 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.027 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".